Cognitive distortions, distorted patterns of thinking, have been increasingly studied in computational mental health research. Although they are related to many, if not all, mental health disorders, most existing studies focus primarily on depression. In this work, we explore distortion profiles across multiple mental health conditions. We analyzed a large Reddit-based dataset containing posts from ten self-reported mental health groups as well as a control group using both an n-gram-based method and a fine-tuned transformer model for detecting cognitive distortions. The mental health groups, both when pooled together and when examined individually, show a higher prevalence of cognitive distortions compared to the control group, with the effect sizes ranging from small to moderate. When comparing distortion profiles of different mental health conditions, we observe largely similar patterns, but with some conditions showing an overall higher frequency of distortions than others. These findings suggest that even relatively simple methods can be suitable for exploratory analyses that reveal group-level trends in large-scale mental health text data.