Usability is a key factor in the effectiveness of recommender systems. However, the analysis of user interfaces is a time-consuming process that requires expertise. Recent advances in multimodal large language models (LLMs) offer promising opportunities to automate such evaluations. In this work, we explore the potential of multimodal LLMs to assess the usability of recommender system interfaces by considering a variety of publicly available systems as examples. We take user interface screenshots from multiple of these recommender platforms to cover both preference elicitation and recommendation presentation scenarios. An LLM is instructed to analyze these interfaces with regard to different usability criteria and provide explanatory feedback. Our evaluation demonstrates how LLMs can support heuristic-style usability assessments at scale to support the improvement of user experience.