In traditional Multi-Agent Path Finding (MAPF) problems, environments are typically represented as grid graphs. However, due to the high computational cost in continuous time domains, recent studies have employed roadmap graphs. This study assesses how different methods for generating roadmap graphs affect path planning. Through simulations in static environments such as factories and warehouses, we observed a balance in performance based on the attributes of each method. Notably, overlooking rotational costs in graph searches can lead to significantly higher path costs. Incorporating these insights, we devised graph generation techniques that markedly enhance path planning efficiency.