In cone beam computed tomography (CBCT)-guided adaptive radiotherapy, rapid and precise segmentation of organs-at-risk (OARs) is essential for accurate dose verification and online replanning. The quality of CBCT images obtained with current onboard CBCT imagers and clinical imaging protocols, however, is often compromised by artifacts such as scatter and motion, particularly for thoracic CBCT scans. These artifacts not only degrade image contrast but also obscure anatomical boundaries, making accurate segmentation on CBCT images significantly more challenging compared to planning CT images. To address these persistent challenges, we propose a novel multi-channel conditional consistency diffusion model (MCCDM) for segmentation of OARs in thoracic CBCT images (CBCT-MCCDM), which harnesses its domain transfer capabilities to improve segmentation accuracy across different imaging modalities. By jointly training the MCCDM with CT images and their corresponding masks, our framework enables an end-to-end mapping learning process that generates accurate segmentation of OARs. This CBCT-MCCDM was used to delineate esophagus, heart, left and right lungs, and spinal cord on CBCT images from patients receiving radiation therapy. We quantitatively evaluated our approach by comparing model-generated contours with ground truth contours from 33 patients with lung or metastatic cancers treated with 5-fraction stereotactic body radiation therapy (SBRT), demonstrating its potential to enhance segmentation accuracy despite the presence of challenging CBCT artifacts. The proposed method was evaluated using average Dice similarity coefficients (DSC), sensitivity, specificity, 95th Percentile Hausdorff Distance (HD95), and mean surface distance (MSD) for each of the five OARs. The method achieved average DSC values of 0.82, 0.88, 0.95, 0.96, and 0.96 for the esophagus, heart, left lung, right lung, and spinal cord, respectively. Sensitivity values were 0.813, 0.922, 0.956, 0.958, and 0.929, respectively, while specificity values were 0.991, 0.994, 0.996, 0.996, and 0.995, respectively. We compared the proposed method with two state-of-art methods, CBCT-only method and U-Net, and demonstrated that the proposed CBCT-MCCDM method achieved superior performance across all metrics.