A major aspect of Cyber-physical production systems is to increase adaptability regarding the physical structure of manufacturing components as well as of the applied software systems. This is motivated by the increasing engineering efforts of manufacturing systems due to changing requirements, varying product variants and also malfunctions. In context of the method plug and produce physical adaptability, the customization of the plant layout of a production system, is an integral part. Physical adaptions become necessary when e.g. the production process is changed, the process performance requires to be optimized or manufacturing components need to be replaced. Consequently, a technique for determining appropriate manufacturing modules for a given production process is needed. In contrast to current methods this paper presents a novel approach for physical adaptability especially respecting the discrete system behavior of manufacturing modules. For this purpose, subgraph isomorphism is combined with a semantic matching filter together with additional matching criteria. Both, matching filter and matching criteria are tailored to the use case of plug and produce. Based on a case-study the proposed method is evaluated. The matching quality is assessed using macro-averaged precision and the normalized discounted cumulative gain.