Empirical insights about business models in the field of e-mobility services are of high importance to academia, industry and politics. As basic clustering algorithms do not deliver semantically valuable findings on business model structures based on obtained empiric data, this paper proposes a similarity measure-based network approach of clustering the latter. On the basis of graph, social network and similarity measure theory, an approach is designed which compares every business model instances of a data set with each other. The paper comes up with a matching score in order to determine whether two business models are connected contentwise within a cluster or not. The plotting of the resulting matching scores leads to a visually based determination of a meaningful matching score which bonds two business models together or not. The elaborations result in four e-mobility service clusters: Dataand-software-driven-, brokering-, transportation- and energy supply-based business models. Additionally, further findings on current opportunities in clustering business models and future solution proposals are described.