Conformance checking is a sub-discipline of process mining, which compares process execution data with predefined process models to identify deviations between them. Although recognized as the most important feature of process mining tools, conformance checking is currently not widely applied in practice. One reason for this lack of adoption is the absence of process-mining-specific visualizations, which can effectively communicate conformance checking results to practitioners. Although researchers have identified the need for such visualizations, they have left their development to the tool providers, such that available visualizations are highly different and difficult to compare. This inhibits the opportunities to conduct empirical research on conformance checking visualizations, which would be crucial to understanding user preferences. To address this issue and establish a foundation for future empirical research, this paper provides an overview of the existing breadth of characteristics of conformance checking visualizations in the form of a taxonomy. This taxonomy consists of six dimensions, which highlight in a structured manner what information is displayed in conformance checking visualizations and how this is visualized in different academic and commercial tools. Our research enhances the comprehension of visual analytics in process mining, particularly for conformance checking, and highlights promising avenues for future empirical research.