Answering questions in Gaokao (the national college entrance examination in China) brings a great challenge for recent AI systems, where the difficulty of questions and the lack of formal knowledge are two main obstacles, among others. In this paper, we focus on answering multiple-choice questions in geographical Gaokao. Specifically, a concept graph for geographical Gaokao is automatically constructed from textbook tables and Chinese wiki encyclopedia, to capture the core concepts and relations in geography. Based on this concept graph, a graph search based question answering approach is designed to find explainable inference paths between questions and answer choices. We developed an online system called CGQA and conducted experiments on two real datasets created from the last ten year geographical Gaokao. Our experiments show that CGQA generates accurate judgments and provides explainable solving procedures. Additionally, CGQA shows promising improvement by combining with existing approaches.